Statistics PhD candidate Nathan Yau authors the popular blog FlowingData and has now branched off and written a useful reference text. Visualize this will serve as the basis of most of our tutorial work this semester – we'll be working with Nathan's thorough R tutorials that show how the statistical visualization language can be used to visualize data. While R is not the required platform for final projects – each of you will be expected to complete the labs and play around with the examples you've been provided and inputting some alternate data sets, altering the code slightly, etc. The structure of Nathan's text has largely informed our weekly lecture topics as well, so make sure to read closely as Nathan really has a handle on the intersection of stats and design.
Jenn & Ken Visocky O'Grady have written a masterful (and accessible) survey of the discipline of information design. While this text is not formally required, it is highly recommended for reflection on best practices and important precedents (the book is jammed with great case studies) within design. Reading and thinking about this book will really assist you in planning and executing the design projects within the course. It also compliments Nathan's more pragmatic, exercise based topics quite nicely.
Bar none, Manuel Lima (of Visual Complexity fame) has organize the definitive collection of contemporary visualization projects and thoughtfully categorized them by topic. This is a thinking man's coffee table book and would provide an abundance of inspiration for the major assignments within the course. If you can't afford, don't want to purchase the book – at least spend some time browsing Lima's impressive blog.
Visualize This: The Flowing Data Guide to Design, Visualization and Statistics
Statistics PhD candidate Nathan Yau authors the popular blog FlowingData and has now branched off and written a useful reference text. Visualize this will serve as the basis of most of our tutorial work this semester – we'll be working with Nathan's thorough R tutorials that show how the statistical visualization language can be used to visualize data. While R is not the required platform for final projects – each of you will be expected to complete the labs and play around with the examples you've been provided and inputting some alternate data sets, altering the code slightly, etc. The structure of Nathan's text has largely informed our weekly lecture topics as well, so make sure to read closely as Nathan really has a handle on the intersection of stats and design.
Highly Recommended:
The Information Design Handbook
Jenn & Ken Visocky O'Grady have written a masterful (and accessible) survey of the discipline of information design. While this text is not formally required, it is highly recommended for reflection on best practices and important precedents (the book is jammed with great case studies) within design. Reading and thinking about this book will really assist you in planning and executing the design projects within the course. It also compliments Nathan's more pragmatic, exercise based topics quite nicely.
Recommended:
Visual Complexity: Mapping Patters of Information
Bar none, Manuel Lima (of Visual Complexity fame) has organize the definitive collection of contemporary visualization projects and thoughtfully categorized them by topic. This is a thinking man's coffee table book and would provide an abundance of inspiration for the major assignments within the course. If you can't afford, don't want to purchase the book – at least spend some time browsing Lima's impressive blog.